Iterative Forecasting with Nested Ensembles • modeltime.ensemble

Iteratively forecast with nested modeling

Why is nested forecasting important? For starters, the ability to iteratively forecast time series with many models that are trained on many individual groups has been a huge request from students in our Time Series Course. Why? Because two methods exist that get results:

Global Modeling: Best for scalability using a Global Models and a Panel Data structure. See Forecasting with Global Models.
Iterative Forecasting: Best for accuracy using a Nested Data Structure. Takes longer than global model (more resources due to for-loop iteration), but can yield great results.

We’ve incorporated a new approach called “nested forecasting” to help perform Iterative Forecasting.

What is Nested Forecasting?

The core idea of nested forecasting is to convert a dataset containing many time series groups into a nested data set, then fit many models to each of the nested datasets. The result is an iterative forecasting process that generates Nested Modeltime Tables with all of the forecast attributes needed to make decisions.

What is Nested Ensembling?

Nested ensembling applies the concept of ensembling, which is generally averaging many individual models (called submodels) to produce a more stable model that sometimes improves over the best individual model.

We can apply the ensembling techniques to iterative or nested forecasting. In this tutorial, we will show you how to perform:

Average Ensembles using ensemble_nested_average(). These are the simplest models.
Weighted Ensembles using ensemble_nested_weighted(). These allow the user to provide “loadings” to distribute the weighting to the top models, which can sometimes improve over the simple average ensembles.

Let’s go!

Nested Ensemble Tutorial

We’ll showcase nested ensembling for iterative forecasting in this short tutorial.

Libraries

Load the following libraries.

library(tidymodels)
library(modeltime)
library(modeltime.ensemble)
library(dplyr)
library(timetk)
library(gt)

Data

Read in the Walmart Sales Weekly data (comes with timetk).

data_tbl <- walmart_sales_weekly %>%
    select(id, date = Date, value = Weekly_Sales) %>%
    filter(id %in% c("1_1", "1_3"))

data_tbl

#> # A tibble: 286 × 3
#>    id    date        value
#>    <fct> <date>      <dbl>
#>  1 1_1   2010-02-05 24924.
#>  2 1_1   2010-02-12 46039.
#>  3 1_1   2010-02-19 41596.
#>  4 1_1   2010-02-26 19404.
#>  5 1_1   2010-03-05 21828.
#>  6 1_1   2010-03-12 21043.
#>  7 1_1   2010-03-19 22137.
#>  8 1_1   2010-03-26 26229.
#>  9 1_1   2010-04-02 57258.
#> 10 1_1   2010-04-09 42961.
#> # ℹ 276 more rows

We can get a quick visual of the two time series we will forecast.

data_tbl %>%
  group_by(id) %>%
  plot_time_series(date, value, .facet_ncol = 1, .interactive = FALSE)

Prepare the Data in Nested Format

The most critical stage in “Nested Forecasting” is data preparation, making sure that the input to the nested forecasting workflow is in the appropriate structure. We’ve included several functions to help that involve a bit of forethought that can be broken into 3 steps:

Extending each of the times series: How far into the future do you need to predict for each time series? See extend_timeseries().
Nesting by the grouping variable: This is where you create the nested structure. You’ll identify the ID column that separates each time series, and the number of timestamps to include in the “.future_data” and optionally “.actual_data”. Typically, you’ll select the same .length_future as your extension from the previous step. See nest_timeseries().
Train/Test Set Splitting: Finally, you’ll take your .actual_data and convert into train/test splits that can be used for accuracy and confidence interval estimation. See split_nested_timeseries().

Here are the 3-steps in action:

nested_data_tbl <- data_tbl %>%
    # Step 1: Extend
    extend_timeseries(
        .id_var        = id,
        .date_var      = date,
        .length_future = 52
    ) %>%
    # Step 2: Nest
    nest_timeseries(
        .id_var        = id,
        .length_future = 52,
        .length_actual = 52*2
    ) %>%
    # Step 3: Split Train/Test
    split_nested_timeseries(
        .length_test = 52
    )

nested_data_tbl

#> # A tibble: 2 × 4
#>   id    .actual_data       .future_data      .splits        
#>   <fct> <list>             <list>            <list>         
#> 1 1_1   <tibble [104 × 2]> <tibble [52 × 2]> <split [52|52]>
#> 2 1_3   <tibble [104 × 2]> <tibble [52 × 2]> <split [52|52]>

Nested Modeltime Workflow

Next, we move into the Nested Modeltime Workflow now that nested data has been created. The Nested Modeltime Workflow includes 3 steps:

Modeling Fitting: This is the training stage where we fit to training data. The test forecast is generated from this step.
1. Create tidymodels workflows.
2. modeltime_nested_fit(): Used to fit the submodels to the training data.
3. ensemble_nested_average() or ensemble_nested_weighted(): Used to make ensembles from the submodels.
Model Evaluation and Selection: This is where we review model performance and select the best model by minimizing or maximizing an error metric. See modeltime_nested_select_best().
Model Refitting: This is the final fitting stage where we fit to actual data. The future forecast is generated from this step. See modeltime_nested_refit().

Step 1A: Create Tidymodels Workflows

First, we create tidymodels workflows for the various models that you intend to create.

Prophet

A common modeling method is prophet, that can be created using prophet_reg(). We’ll create a workflow. Note that we use the extract_nested_train_split(nested_data_tbl) to help us build preprocessing features.

rec_prophet <- recipe(value ~ date, extract_nested_train_split(nested_data_tbl)) 

wflw_prophet <- workflow() %>%
    add_model(
        prophet_reg("regression", seasonality_yearly = TRUE) %>% 
            set_engine("prophet")
    ) %>%
    add_recipe(rec_prophet)

XGBoost

Next, we can use a machine learning method that can get good results: XGBoost. We will add a few extra features in the recipe feature engineering step to generate features that tend to get better modeling results. Note that we use the extract_nested_train_split(nested_data_tbl) to help us build preprocessing features.

rec_xgb <- recipe(value ~ ., extract_nested_train_split(nested_data_tbl)) %>%
    step_timeseries_signature(date) %>%
    step_rm(date) %>%
    step_zv(all_predictors()) %>%
    step_dummy(all_nominal_predictors(), one_hot = TRUE)

wflw_xgb <- workflow() %>%
    add_model(boost_tree("regression") %>% set_engine("xgboost")) %>%
    add_recipe(rec_xgb)

Step 1B: Nested Modeltime Tables

With a couple of modeling workflows in hand, we are now ready to test them on each of the time series. We start by using the modeltime_nested_fit() function, which iteratively fits each model to each of the nested time series train/test “.splits” column.

nested_modeltime_tbl <- modeltime_nested_fit(
  # Nested data 
  nested_data = nested_data_tbl,
  
  # Add workflows
  wflw_prophet,
  wflw_xgb
)

#> Fitting models on training data... ■■■■■■■■■■■■■■■■                  50% | ETA:…

#> Fitting models on training data... ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■  100% | ETA:…

nested_modeltime_tbl

#> # Nested Modeltime Table
#>

#> Trained on: .splits | Model Errors: [0]

#> # A tibble: 2 × 5
#>   id    .actual_data       .future_data      .splits         .modeltime_tables 
#>   <fct> <list>             <list>            <list>          <list>            
#> 1 1_1   <tibble [104 × 2]> <tibble [52 × 2]> <split [52|52]> <mdl_tm_t [2 × 5]>
#> 2 1_3   <tibble [104 × 2]> <tibble [52 × 2]> <split [52|52]> <mdl_tm_t [2 × 5]>

This adds a new column with .modeltime_tables for each of the data sets and has created several logged attributes that are part of the “Nested Modeltime Table”. We also can see that the models were trained on “.splits” and none of the models had any errors.

Accuracy Check

This is kind of advanced, but because our accuracy functions (table_modeltime_accuracy(.interactive = FALSE)) produce static gt table, we can make a function to highlight rows by group.

tab_style_by_group <- function(object, ..., style) {
  
  subset_log <- object[["_boxhead"]][["type"]]=="row_group"
  grp_col    <- object[["_boxhead"]][["var"]][subset_log] %>% rlang::sym()
  
  object %>%
    tab_style(
      style = style,
      locations = cells_body(
        rows = .[["_data"]] %>%
          tibble::rowid_to_column("rowid") %>%
          group_by(!!grp_col) %>%
          filter(...) %>%
          ungroup() %>%
          pull(rowid)
      )
    )
}

And now we can see which models are the winners, performing the best by group with the lowest RMSE (root mean squared error).

nested_modeltime_tbl %>% 
  extract_nested_test_accuracy() %>%
  group_by(id) %>%
  table_modeltime_accuracy(.interactive = FALSE) %>%
  tab_style_by_group(
    rmse == min(rmse),
    style = cell_fill(color = "lightblue")
  )

.model_id	.model_desc	.type	mae	mape	mase	smape	rmse	rsq
Accuracy Table
1_1
1	PROPHET	Test	10071.47	45.88	1.99	59.97	11776.87	0.38
2	XGBOOST	Test	6223.73	25.25	1.23	24.50	9026.26	0.19
1_3
1	PROPHET	Test	3539.80	29.87	1.37	25.46	4707.77	0.80
2	XGBOOST	Test	3071.25	18.74	1.19	20.26	5069.27	0.79

Step 1C: Make Ensembles

Now that we’ve fitted submodels, our goal is to improve on the submodels by leveraging ensembles.

Average Ensemble

We’ll give a go at an average ensemble using a simple mean with the ensemble_nested_average() function. We select type = "mean" for simple average (another option is median ensemble, which is better when you have models with large spikes).

nested_ensemble_1_tbl <- nested_modeltime_tbl %>%
    ensemble_nested_average(
        type           = "mean", 
        keep_submodels = TRUE
    )

nested_ensemble_1_tbl

#> # Nested Modeltime Table
#>   # A tibble: 2 × 5
#>   id    .actual_data       .future_data      .splits         .modeltime_tables 
#>   <fct> <list>             <list>            <list>          <list>            
#> 1 1_1   <tibble [104 × 2]> <tibble [52 × 2]> <split [52|52]> <mdl_tm_t [3 × 5]>
#> 2 1_3   <tibble [104 × 2]> <tibble [52 × 2]> <split [52|52]> <mdl_tm_t [3 × 5]>

We can check the accuracy again. This time the Ensemble (MEAN) outperforms both the prophet and xgboost submodels.

nested_ensemble_1_tbl %>% 
  extract_nested_test_accuracy() %>%
  group_by(id) %>%
  table_modeltime_accuracy(.interactive = FALSE) %>%
  tab_style_by_group(
    rmse == min(rmse),
    style = cell_fill(color = "lightblue")
  )

.model_id	.model_desc	.type	mae	mape	mase	smape	rmse	rsq
Accuracy Table
1_1
1	PROPHET	Test	10071.47	45.88	1.99	59.97	11776.87	0.38
2	XGBOOST	Test	6223.73	25.25	1.23	24.50	9026.26	0.19
3	ENSEMBLE (MEAN): 2 MODELS	Test	5411.95	20.18	1.07	22.20	8654.09	0.41
1_3
1	PROPHET	Test	3539.80	29.87	1.37	25.46	4707.77	0.80
2	XGBOOST	Test	3071.25	18.74	1.19	20.26	5069.27	0.79
3	ENSEMBLE (MEAN): 2 MODELS	Test	2659.68	18.99	1.03	17.74	4037.20	0.82

Weighted Ensemble

Next, we can give a go at a weighted ensemble with the ensemble_nested_weighted() function. A few key points about the arguments:

loadings: This parameter allows us to weight models differently. Providing c(2,1) places a 2-to-1 weighting on the two submodels.
metric: This parameter is determined by the accuracy table. The default is to use the “rmse” column. The loadings are then applied to the best (lowest) “rmse” first. The best model will have 2/3 (66% weight) loading and the second best will have 1/3 (33% weight).
model_ids: This is a filtering mechanism to help us isolate which model ID’s that we want to include as submodels. We want to exclude Model ID 3, because this is our Ensemble Average (MEAN) model.
control: This uses control_nested_fit() to control aspects of the fitting process like running in Parallel vs Sequential and outputting verbose to provide additional information during the fitting process.

nested_ensemble_2_tbl <- nested_ensemble_1_tbl %>%
    ensemble_nested_weighted(
        loadings        = c(2,1),  
        metric          = "rmse",
        model_ids       = c(1,2), 
        control         = control_nested_fit(allow_par = FALSE, verbose = TRUE)
    )

#> ℹ [1/2] Starting Modeltime Table: ID 1_1...

#> ✔ Model 4 Passed ENSEMBLE WEIGHTED.

#> ✔ [1/2] Finished Modeltime Table: ID 1_1

#> ℹ [2/2] Starting Modeltime Table: ID 1_3...

#> ✔ Model 4 Passed ENSEMBLE WEIGHTED.

#> ✔ [2/2] Finished Modeltime Table: ID 1_3

#> Finished in: 1.335346 secs.

nested_ensemble_2_tbl

#> # Nested Modeltime Table
#>

#> Trained on: .splits | Model Errors: [0]

#> # A tibble: 2 × 5
#>   id    .actual_data       .future_data      .splits         .modeltime_tables 
#>   <fct> <list>             <list>            <list>          <list>            
#> 1 1_1   <tibble [104 × 2]> <tibble [52 × 2]> <split [52|52]> <mdl_tm_t [4 × 5]>
#> 2 1_3   <tibble [104 × 2]> <tibble [52 × 2]> <split [52|52]> <mdl_tm_t [4 × 5]>

Next, let’s check the accuracy on the new ensemble. The Weighted Ensemble has improved the 1_1 time series, but not the 1_3 time series.

nested_ensemble_2_tbl %>% 
  extract_nested_test_accuracy() %>%
  group_by(id) %>%
  table_modeltime_accuracy(.interactive = FALSE) %>%
  tab_style_by_group(
    rmse == min(rmse),
    style = cell_fill(color = "lightblue")
  )

.model_id	.model_desc	.type	mae	mape	mase	smape	rmse	rsq
Accuracy Table
1_1
1	PROPHET	Test	10071.47	45.88	1.99	59.97	11776.87	0.38
2	XGBOOST	Test	6223.73	25.25	1.23	24.50	9026.26	0.19
3	ENSEMBLE (MEAN): 2 MODELS	Test	5411.95	20.18	1.07	22.20	8654.09	0.41
4	ENSEMBLE (WEIGHTED): 2 MODELS	Test	4398.23	14.67	0.87	15.90	8322.65	0.41
1_3
1	PROPHET	Test	3539.80	29.87	1.37	25.46	4707.77	0.80
2	XGBOOST	Test	3071.25	18.74	1.19	20.26	5069.27	0.79
3	ENSEMBLE (MEAN): 2 MODELS	Test	2659.68	18.99	1.03	17.74	4037.20	0.82
4	ENSEMBLE (WEIGHTED): 2 MODELS	Test	2776.66	21.31	1.08	19.17	4069.13	0.82

Step 2: Select Best

Using the accuracy data, we can pick a metric and select the best model based on that metric. The available metrics are in the default_forecast_accuracy_metric_set(). Make sure to select minimize based on the metric. The filter_test_forecasts parameter tells the function to filter the logged test forecasts to just the best.

best_nested_modeltime_tbl <- nested_ensemble_2_tbl %>%
    modeltime_nested_select_best(
      metric                = "rmse", 
      minimize              = TRUE, 
      filter_test_forecasts = TRUE
    )

Extract Nested Best Model Report

The best model selections can be accessed with extract_nested_best_model_report().

best_nested_modeltime_tbl %>%
  extract_nested_best_model_report() %>%
  table_modeltime_accuracy(.interactive = FALSE)

id	.model_id	.model_desc	.type	mae	mape	mase	smape	rmse	rsq
Accuracy Table
1_1	4	ENSEMBLE (WEIGHTED): 2 MODELS	Test	4398.23	14.67	0.87	15.90	8322.65	0.41
1_3	3	ENSEMBLE (MEAN): 2 MODELS	Test	2659.68	18.99	1.03	17.74	4037.20	0.82

Extract Nested Best Test Forecasts

Once we’ve selected the best models, we can easily visualize the best forecasts by time series. Note that the nested test forecast logs have been modified to isolate the best models.

best_nested_modeltime_tbl %>%
  extract_nested_test_forecast() %>%
  group_by(id) %>%
  plot_modeltime_forecast(
    .facet_ncol  = 1,
    .interactive = FALSE
  )

Step 3: Refitting and Future Forecast

With the best models in hand, we can make our future forecasts by refitting the models to the full dataset.

If the best models have been selected, the only the best models will be refit.
If best models have not been selected, then all models will be refit.

We’ve selected our best models, and will move forward with refitting and future forecast logging using the modeltime_nested_refit() function.

nested_modeltime_refit_tbl <- best_nested_modeltime_tbl %>%
    modeltime_nested_refit(
        control = control_nested_refit(verbose = TRUE)
    )

#> ℹ [1/2] Starting Modeltime Table: ID 1_1...

#> ✔ Model 4 Passed ENSEMBLE (WEIGHTED): 2 MODELS.

#> ✔ [1/2] Finished Modeltime Table: ID 1_1

#> ℹ [2/2] Starting Modeltime Table: ID 1_3...

#> ✔ Model 3 Passed ENSEMBLE (MEAN): 2 MODELS.

#> ✔ [2/2] Finished Modeltime Table: ID 1_3

#> Finished in: 0.9195981 secs.

We can see that the nested modeltime table appears the same, but has now been trained on .actual_data.

nested_modeltime_refit_tbl

#> # Nested Modeltime Table
#>

#> Trained on: .actual_data | Model Errors: [0]

#> # A tibble: 2 × 5
#>   id    .actual_data       .future_data      .splits         .modeltime_tables 
#>   <fct> <list>             <list>            <list>          <list>            
#> 1 1_1   <tibble [104 × 2]> <tibble [52 × 2]> <split [52|52]> <mdl_tm_t [1 × 5]>
#> 2 1_3   <tibble [104 × 2]> <tibble [52 × 2]> <split [52|52]> <mdl_tm_t [1 × 5]>

Extract Nested Future Forecast

After the refitting process completes, we can now access the future forecast, which is logged.

nested_modeltime_refit_tbl %>%
  extract_nested_future_forecast() %>%
  group_by(id) %>%
  plot_modeltime_forecast(
    .interactive = FALSE,
    .facet_ncol  = 2
  )

Summary

Nested ensembling is a powerful technique that can improve forecasting accuracy. But, this is just a small portion of what can be done to take your forecasting to the next level… If you want to become a forecasting expert for your organization, then take the read on!

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